Designing, modeling, and evaluation of improved cropping strategies and multi-level interactions in intercropping systems in the North China Plain

Adjusting cropping systems in order to increase their efficiency is a global issue. High yield and sustainability are the catchphrases of production in the 21st century, and agricultural production has to solve the balancing act between ecology and economy. Therefore, the requests for farmers, consultants and researchers are rising, and production modes are changing. Nevertheless, solutions have to be detected spatially explicit and locally adapted and accepted in order to be implemented successfully. Taking the North China Plain as an example, the productivity of arable land needs to be further increased by applying strategies to reduce or avoid negative environmental effects. Further yield increases are not possible by increasing input factors like N-fertilizer or irrigation water as N-fertilizer rates are extremely high and irrigation water is limited. However, yield increases might be possible by developing improved cropping strategies operated by cropping designs. Taking modeling and simulation tools into account back up the acceleration of research attainments and the understanding of cropping systems. The present thesis embraces the designing and modeling of such a potential cropping system, to wit strip intercropping. Thus, the main goals of the study were to analyze, design, evaluate, and in the end model intercropping. Intercropping systems are complex systems which strongly need to be designed and evaluated carefully in order to fulfill the premises of ecological and economical efficiency as well as sustainability. Multi-level interactions have to be weighted and taken into regard for evaluating datasets applicative for modeling and simulating intercropping. The main results of the study indicated, that traditional cropping systems like intercropping are widespread in China, where approximately one third of arable land is under intercropping. Reviewing cereal intercropping systems in China, the four agro-ecological regions ?Northeast and North?, the ?Northwest?, the ?Yellow-Huai River Valley? and the ?Southwest? could be classified, distinguished and described. Intercropping offers a great variation of species combination, benefits as well as challenges for cropping systems design and farmers. Carefully balanced between facilitation and competition, intercropping bears the potential of increased yield and yield stability, income security, resource use efficiency and biodiversity. Intercropping gives evidence about traditional cropping systems with the potential for future production systems under the paradigm of sustainability. Further, results from conducted field experiments indicated that border effects are the key component of intercropping performance. Nevertheless, analyzing strip intercropping statistically has peculiarities as they lack in randomization because the cropping system imposes alternating strips. Thus, spatial variability and its effect on yield were regarded differently within a geo-statistical analysis. In addition to the geo-statistical analysis, the crop growth modeling approach paid tribute to monocropping effects as well as to field border effects occurring in strip intercropping systems. Further on a model-based approach was tested to quantify multi-level interactions with special regard to changing microclimatic conditions and to optimize intercropping systems from an agronomical point of view. In comparison to other interspecific competition modeling approaches, a shading algorithm was evaluated and implemented into the process-oriented crop growth model DSSAT in order to simulate competition for solar radiation. More common in modeling mixed intercropping, a modified Beer?s law subroutine has been used instead, e.g. in APSIM. APSIM and DSSAT were compared by modeling the conducted field trials. As a result, the Beer?s law approach was not capable to model strip intercropping. In contrast, the modeling with a changed DSSAT model showed that applying a simple shading algorithm that estimated the proportion of shading in comparison to the monocropping situation and in dependency from neighboring plant height seems to be a promising approach. The results indicated that competition for solar radiation in those systems is a driving force for crop productivity but neither the most dominant nor the one and only. Resource distribution and allocation in space and time seems to be more important than the total amount of resources. Those effects have to be taken into account when simulating interspecific competition.Definiert als der Anbau von zwei oder mehr Feldfrüchten auf der gleichen Fläche und innerhalb der gleichen oder einer sich überlappenden Vegetationsperiode, bietet Intercropping eine große Bandbreite an Kombinationsmöglichkeiten von Feldfrüchten, verbunden mit vorteilhaften und nachhaltigen Effekten für die jeweiligen Kulturarten. Intercropping ist aber gleichzeitig eine Herausforderung für jeden Landwirt und stellt hohe Ansprüche an die Gestaltung des jeweiligen Produktions- oder Anbausystems. Intercropping ist in China weit verbreitet. Schätzungen zufolge wird Intercropping auf rund einem Drittel der gesamten Anbaufläche praktiziert. Intercropping gilt als ein Anbausystem, welches bei geringerem Betriebsmitteleinsatz höhere Erträge oder Gewinne erzielt, verglichen mit den ausgedehnten Monocropping Systemen moderner Agrar-Industriebetriebe. Damit belegt Intercropping, dass in traditionellen Anbausystemen ein Potential für zukünftige und nachhaltige Produktionssysteme schlummert. Um diesen Paradigmen und um politischen, sozialen und ökonomischen Prämissen gerecht zu werden, muss die Agrarforschung Lösungen und Strategien für angepasste Produktionssysteme bereitstellen ? und das in immer kürzeren Zeitspannen. Der Einsatz von computergestützten Pflanzenwachstumsmodellen, mit deren Hilfe komplexe Anbausysteme regional und überregional, sowie über längere Zeiträume hinweg simuliert und analysierte werden können, hat sich dabei als wertvoll erwiesen. Wie Intercropping Systeme gestaltet werden müssen und welche Probleme dabei auftauchen, welche Datengrundlage für eine Modellierung benötigt wird und welche systemimmanenten Interaktionen berücksichtig werden müssen, sind Gegenstand der vorliegenden Dissertation. Allerdings gestaltet sich die statistische Auswertung von speziell Strip Intercropping als schwierig, da Intercropping-Versuche aufgrund der zwangsläufig streifenförmigen Anordnung nicht randomisiert werden können. Intercropping bedarf also einer räumlichen Betrachtungsweise, um ertragsrelevante Effekte adäquat abzuschätzen und statistisch abzusichern. Deshalb wurden die Versuche geostatistisch ausgewertet und mehrere räumliche Modelle evaluiert und getestet, um die Modellgüte zu verbessern. Nicht nur die statistische Auswertung von Intercropping ist diffizil, auch die Datengrundlage von Intercropping in China ist lückenhaft. Im Vergleich zu anderen Ländern wie beispielsweise Indien oder Teilen Afrikas, wo Intercropping gängige Praxis ist, scheint die Dokumentation und Erforschung von Intercropping Systemen in China Nachholbedarf zu haben. In einer Literaturstudie wurde deshalb ein erster Versuch unternommen, China in agro-klimatische Regionen hinsichtlich ihres Potentials und ihrer Verbreitung von Getreide betonten Intercropping Systemen einzuteilen. In einer zweiten Literaturstudie wurde dargestellt, welche Modelle für Intercropping bereits evaluiert, kalibriert und validiert wurden. Exemplarisch für ein prozess-orientiertes Pflanzenwachstumsmodell, welches multiple Anbausysteme und deren Konkurrenz um Sonnenlicht mithilfe des Beer-Lambert?schen Gesetzes simuliert, wurde APSIM gewählt. Dieser in der Forschung recht gängige Ansatz wurde mit dem in der vorliegenden Dissertation evaluierten, getesteten und in DSSAT implementierten Beschattungs-Algorithmus verglichen. Mit dem DSSAT Modell war es bislang nicht möglich, Intercropping zu simulieren. Es zeigte sich, dass es mit einem modifizierten Beer-Lambert?schen Gesetz nicht möglich war, Strip Intercropping adäquat zu simulieren. Unter der Voraussetzung, dass es im Strip Intercropping einen Gewinner und einen Verlierer gibt, das heißt, dass eine Kulturart mehr Sonnenlicht erhält als im Monocropping und eine andere dafür weniger, ist der Beer-Lambert?sche Ansatz viel versprechend und verwendbar. Die Kompensationsfähigkeit einer Fruchtart kann jedoch nicht simuliert werden, ebenso keine Ertragssteigerung der im System dominanten Fruchtart. Im Gegensatz dazu zeigte sich, dass der Beschattungs-Algorithmus, der in DSSAT integriert wurde, beide Systeme ? Intercropping und Monocropping ? simulieren konnte. Allerdings wurde in diesem Ansatz zusätzlich berücksichtig und getestet, dass Konkurrenz um solare Einstrahlung nicht die einzig bestimmende ist. Der Beschattungs-Algorithmus konnte zwar einen Teil des Ertragszuwachses im Intercropping erklären beziehungsweise simulieren, allerdings erst unter Berücksichtigung mikroklimatischer Effekte. Der Allokation von Pflanzenwachstumsfaktoren in Raum und Zeit kommt in Intercropping Systemen eine größere Rolle zu als deren absolute Höhe oder Menge. Solche Effekte müssen berücksichtig werden, um die Modellierung von Strip Intercropping weiterhin zu verbessern und Strip Intercropping Systeme zu optimieren

Utilización de DSSAT para simular el rendimiento potencial de maní en la región central de Córdoba

El crecimiento y desarrollo de un cultivo es determinado por su manejo y por rasgos genéticos que interactúan con factores ambientales. El objetivo de este trabajo fue simular rendimientos potenciales del cultivar de maní ASEM 485 INTA, creciendo en tres fechas de siembra en Argentina, mediante el uso del modelo CROPGRO-Peanut y determinar los coeficientes genéticos del cultivar calibrados a partir de datos de campo de experimentos desarrollados en Argentina entre 2002 y 2006. La calibración del modelo resultó en coeficientes genéticos que produjeron valores simulados para el desarrollo y el crecimiento con rangos del la raíz del error cuadrático medio (RMSE) entre 1,41 – 2,94 días para fenología (subperíodo siembra-R1 y siembra-R5, respectivamente) y, 874; 445,4 y 266,2 kg/ha para peso seco de la parte aérea, peso seco de las vainas y rendimiento, respectivamente; con coeficientes de determinación (R2) entre 0,82 y 0,99 para todos los rasgos. La estimación precisa de los coeficientes genéticos para la variedad de maní ASEM 485 INTA permite el empleo del modelo CROPGRO-Peanut para tal cultivar en la principal región Manisera de Argentina.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Utilización de DSSAT para simular el rendimiento potencial de maní en la región central de Córdoba

El crecimiento y desarrollo de un cultivo es determinado por su manejo y por rasgos genéticos que interactúan con factores ambientales. El objetivo de este trabajo fue simular rendimientos potenciales del cultivar de maní ASEM 485 INTA, creciendo en tres fechas de siembra en Argentina, mediante el uso del modelo CROPGRO-Peanut y determinar los coeficientes genéticos del cultivar calibrados a partir de datos de campo de experimentos desarrollados en Argentina entre 2002 y 2006. La calibración del modelo resultó en coeficientes genéticos que produjeron valores simulados para el desarrollo y el crecimiento con rangos del la raíz del error cuadrático medio (RMSE) entre 1,41 – 2,94 días para fenología (subperíodo siembra-R1 y siembra-R5, respectivamente) y, 874; 445,4 y 266,2 kg/ha para peso seco de la parte aérea, peso seco de las vainas y rendimiento, respectivamente; con coeficientes de determinación (R2) entre 0,82 y 0,99 para todos los rasgos. La estimación precisa de los coeficientes genéticos para la variedad de maní ASEM 485 INTA permite el empleo del modelo CROPGRO-Peanut para tal cultivar en la principal región Manisera de Argentina.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Mapping and modeling groundnut growth and productivity in rainfed areas of Tamil Nadu

A research study was conducted at Tamil Nadu Agricultural University, Coimbatore during kharif and rabi 2015 to estimate groundnut area, model growth and productivity and assess the vulnerability of groundnut to drought using remote sensing techniques. Multi temporal Sentinel 1A satellite data at VV and VH polarization with 20 m spatial resolution was acquired from May, 2015 to January, 2016 at 12 days interval and processed using MAPscape-RICE software. Continuous monitoring was done for ground truth on crop parameters in twenty monitoring sites and validation exercise was done for accuracy assessment. Input files on soil, weather and management practices were generated and crop coefficients pertaining to varieties were developed to assess growth and productivity of groundnut using DSSAT CROPGRO-Peanut model. Outputs from remote sensing and DSSAT model were assimilated to generate LAI thereby groundnut yield spatially and validated against observed yields. Being a rainfed crop, vulnerability of groundnut to drought was assessed integrating different meteorological and spectral indices viz., Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI) and Water Requirement Satisfaction Index (WRSI).Spectral dB curve of groundnut was generated using temporal multi date Sentinel 1A data. A detailed analysis of temporal signatures of groundnut showed a minimum at sowing and a peak at pod development stage and decreasing thereafter towards maturity. Groundnut crop expressed a significant temporal behaviour and large dynamic range (-11.74 to -5.31 in VV polarization and -20.04 to -13.05 in VH polarization) during its growth period. Groundnut area map was generated using maximum likelihood classifier integrating multi temporal features with a classification accuracy of 87.2 per cent and a kappa score of 0.74. The total classified groundnut area in the study districts was 88023 ha covering 17817 and 22582 ha in Salem and Namakkal districts during kharif 2015 while Villupuram and Tiruvannamalai districts accounted for 22722 and 24903 ha respectively during rabi 2015. Blockwise statistics on groundnut area during both seasons were also generated. To model growth and productivity of groundnut in DSSAT, weather and soil input files were generated using weatherman and ‘S’ build respectively besides deriving genetic coefficients for CO 6, TMV 7 and VRI 2 varieties of groundnut. Growth and development variables of groundnut were simulated using CROPGROPeanut model i.e., days to emergence (7-9 days) and anthesis (25-32 days), canopy height (63 to 70 cm), maximum LAI (1.12 to 3.07) and biomass (4176 to 9576 kg ha-1 across twenty monitoring locations spatially. The resultant pod yield was simulated to be 1796 to 3060 kg ha-1 with a harvest index of 0.28 to 0.43. On comparison of LAI between observed (2.01 to 4.05) and simulated values (1.12 to 3.07) the CROPGRO-Peanut model was found to under estimate the values with R2, RMSE and NRMSE of 0.82, 1.10 and 34 per cent. However, the model predicted the biomass of groundnut with an agreement of 89 per cent through the simulated values of 4176 to9576 kg ha-1 as against the observed biomass to 4620 to 9959 kg ha-1. The simulated pod yields of groundnut in the study area were 1796 to 3060 kg ha-1 as compared to the observed yields of 2115 to 2750 kg ha-1. The overall agreement between simulated and observed yields was 84 per cent with the average errors of 0.81, 342 kg ha-1 and 16 percent for R2, RMSE and NRMSE respectively. LAI values of groundnut, generated spatially through suitable regression models using dB from satellite images and LAI from DSSAT, ranged from 1.31 to 3.23 with R2, RMSE and NRMSE of 0.86, 0.78 and 24 per cent respectively on comparison with observed values. Remote sensing based spatial estimation resulted in groundnut pod yields of 1570 to 3102 kg ha-1 across the study districts of Salem, Namakkal, Tiruvannamalai and Villupuram. In the 20 monitoring locations, the pod yields were estimated to be 1912 to 2975 kg ha-1 as against the observed pod yields of 1450 to 2750 kg ha-1 with a fairly good agreement of 80 per cent. The vulnerability of groundnut was assessed using different drought indices viz., SPI, NDVI and WRSI. Considering SPI, out of the total groundnut area of 88023 ha, an area of 86607 ha was found to be under near normal condition based on deviation of rainfall received during cropping season from historical precipitation. Similarly NDVI, an indicator of vegetation condition during the cropping season, showed that 14272 ha of groundnut area were under stressed condition during 2015. An area of 40981 ha in Villupuram and Tiruvannamalai districts was found to be under chances of crop failure based on Water Requirement Satisfaction index (WRSI). Major groundnut areas of Salem district (14188 ha) was under medium risk zone. Considering overall vulnerability, whole district of Villupuram was adjudged as highly vulnerable to drought with regard to groundnut cultivation whereas four blocks of Salem, eight blocks of Namakkal and all the blocks of Tiruvannamalai were found to be moderately vulnerable to drought

Maine State Government Administrative Report 1989-1990

https://digitalmaine.com/me_annual_reports/1016/thumbnail.jp

Maine State Government Administrative Report 1988-1989

https://digitalmaine.com/me_annual_reports/1015/thumbnail.jp